es_from_beta_unstd {metaConvert}

Convert an unstandardized regression coefficient and the standard deviation of the dependent variable into several effect size measures

Description

Convert an unstandardized regression coefficient and the standard deviation of the dependent variable into several effect size measures

Usage

es_from_beta_unstd(
  beta_unstd,
  sd_dv,
  n_exp,
  n_nexp,
  smd_to_cor = "viechtbauer",
  reverse_beta_unstd
)

Arguments

beta_unstd

an unstandardized regression coefficient value (binary predictor, no other covariables in the model)

sd_dv

standard deviation of the dependent variable

n_exp

number of participants in the experimental/exposed group.

n_nexp

number of participants in the non-experimental/non-exposed group.

smd_to_cor

formula used to convert the cohen_d value into a coefficient correlation (see details).

reverse_beta_unstd

a logical value indicating whether the direction of the generated effect sizes should be flipped.

Details

This function estimates a Cohen's d (D) and Hedges' g (G) from an unstandardized linear regression coefficient (coming from a model with only one binary predictor), and the standard deviation of the dependent variable. Odds ratio (OR) and correlation coefficients (R/Z) are then converted from the Cohen's d.

The formula used to obtain the Cohen's d is:

N = n\_exp + n\_nexp

sd\_pooled = \sqrt{\frac{sd\_dv^2 * (N - 1) - unstd\_beta^2 * \frac{n\_exp * n\_nexp}{N}}{N - 2}}

cohen\_d = \frac{unstd\_beta}{sd\_pooled}

To estimate other effect size measures, calculations of the es_from_cohen_d() are applied.

Value

This function estimates and converts between several effect size measures.

natural effect size measure	D + G
converted effect size measure	OR + R + Z
required input data	See 'Section 13. (Un-)Standardized regression coefficient'
	https://metaconvert.org/html/input.html

References

Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Sage Publications, Inc.

Examples

es_from_beta_unstd(beta_unstd = 2.1, sd_dv = 0.98, n_exp = 20, n_nexp = 22)